Massive multiple-input multiple-output (m-mimo) wireless distribution system (wds) and related methods for optimizing the m-mimo wds

ABSTRACT

Embodiments of the disclosure relate to a massive multiple-input multiple-output (M-MIMO) wireless distribution system (WDS) and related methods for optimizing the M-MIMO WDS. In one aspect, the M-MIMO WDS includes a plurality of remote units each deployed at a location and includes one or more antennas to serve a remote coverage area. At least one remote unit can have a different number of the antennas from at least one other remote unit in the M-MIMO WDS. In another aspect, a selected system configuration including the location and number of the antennas associated with each of the remote units can be determined using an iterative algorithm that maximizes a selected system performance indicator of the M-MIMO WDS. As such, it may be possible to optimize the selected system performance indicator at reduced complexity and costs, thus helping to enhance user experiences in the M-MIMO WDS.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/621,177 filed on Jun. 13, 2017, the content of which is relied uponand incorporated herein by reference in its entirety, and the benefit ofpriority under 35 U.S.C. § 120 is hereby claimed.

BACKGROUND

The disclosure relates generally to optimizing performance of a wirelessdistribution system (WDS), and more particularly to enhancing WDS systemcapacity by reducing radio frequency (RF) interference among multipleusers and among multiple antenna.

Wireless customers are increasingly demanding digital data services,such as streaming video signals. At the same time, some wirelesscustomers use their wireless communications devices in areas that arepoorly serviced by conventional cellular networks, such as insidecertain buildings or areas where there is little cellular coverage. Oneresponse to the intersection of these two concerns has been the use ofDASs. DASs include remote units configured to receive and transmitcommunications signals to client devices within an antenna range of theremote units. DASs can be particularly useful when deployed insidebuildings or other indoor environments where the wireless communicationsdevices may not otherwise be able to effectively receive RF signals froma source.

In this regard, FIG. 1 illustrates a distribution of communicationsservices to remote coverage areas 100(1)-100(N) of a WDS provided in theform of a DAS 102, wherein ‘N’ is the number of remote coverage areas.These communications services can include cellular services, wirelessservices, such as RF identification (RFID) tracking, Wireless Fidelity(Wi-Fi), local area network (LAN), and wireless LAN (WLAN), wirelesssolutions (Bluetooth, Wi-Fi Global Positioning System (GPS),signal-based, and others) for location-based services, and combinationsthereof, as examples. The remote coverage areas 100(1)-100(N) may beremotely located. In this regard, the remote coverage areas100(1)-100(N) are created by and centered on remote units 104(1)-104(N)connected to a central unit 106 (e.g., a head-end equipment, a head-endcontroller, or a head-end unit). The central unit 106 may becommunicatively coupled to a signal source 108, for example, a basetransceiver station (BTS) or a baseband unit (BBU). In this regard, thecentral unit 106 receives downlink communications signals 110D from thesignal source 108 to be distributed to the remote units 104(1)-104(N).The remote units 104(1)-104(N) are configured to receive the downlinkcommunications signals 110D from the central unit 106 over acommunications medium 112 to be distributed to the respective remotecoverage areas 100(1)-100(N) of the remote units 104(1)-104(N). Each ofthe remote units 104(1)-104(N) may include an RF transmitter/receiverand at least one respective antenna 114(1)-114(N) operably connected tothe RF transmitter/receiver to wirelessly distribute the communicationsservices to client devices 116 within the respective remote coverageareas 100(1)-100(N). The remote units 104(1)-104(N) are also configuredto receive uplink communications signals 110U from the client devices116 in the respective remote coverage areas 100(1)-100(N) to bedistributed to the signal source 108. The size of each of the remotecoverage areas 100(1)-100(N) is determined by the amount of RF powertransmitted by the respective remote units 104(1)-104(N), receiversensitivity, antenna gain, and RF environment, as well as by RFtransmitter/receiver sensitivity of the client devices 116. The clientdevices 116 usually have a fixed maximum RF receiver sensitivity, sothat the above-mentioned properties of the remote units 104(1)-104(N)mainly determine the size of the respective remote coverage areas100(1)-100(N).

The client devices 116 in any of the remote coverage areas 100(1)-100(N)may be running bandwidth-hungry applications, such as high-definition(HD) mobile video, virtual reality (VR), and augmented reality (AR),that drive the demand for high-capacity wireless access. Moreover,multiple client devices 116 may be running such bandwidth-hungryapplications concurrently, thus further increasing the demand for datathroughput in each of the remote coverage areas 100(1)-100(N). As aresult, the wireless communications industry has adopted multiple-inputmultiple-output (MIMO) technology to help meet the increasing bandwidthdemand by the client devices 116. In this regard, each of the remoteunits 104(1)-104(N) may employ multiple antennas to distribute multiplestreams of the downlink communications signals 110D concurrently. Forexample, each of the remote units 104(1)-104(N) may employ two antennasto concurrently transmit two streams of the downlink communicationssignals 110D, thus doubling the data throughput in the remote coverageareas 100(1)-100(N). When the remote units 104(1)-104(N) distribute themultiple streams of the downlink communications signals 110Dconcurrently to multiple client devices 116, the remote units104(1)-104(N) are said to be communicating the downlink communicationssignals 110D based on multiuser MIMO (MU-MIMO) technology.

The MU-MIMO technology can help provide increased data rate/throughput,enhanced reliability, improved energy efficiency, and/or reducedinterference in the remote coverage areas 100(1)-100(N). As such, theMU-MIMO technology has been incorporated into recent and evolvingwireless communications standards, such as long-term evolution (LTE) andLTE-Advanced. However, to fully benefit from the enhancements providedby the MU-MIMO technology, each of the multiple client devices 116 needsto employ an equal number of antennas as the remote units 104(1)-104(N).Unfortunately, it may become more difficult to add additional antennasin the client devices 116 due to space limitations and complexity. As aresult, it may become difficult to scale the MU-MIMO technology beyondthe capabilities of the client device 116. Accordingly, the wirelesscommunications industry is adopting a new antenna technology known asmassive MIMO (M-MIMO), which may scale up the MU-MIMO technology byorders of magnitude, to meet the increasing bandwidth demands by theclient devices 116.

No admission is made that any reference cited herein constitutes priorart. Applicant expressly reserves the right to challenge the accuracyand pertinency of any cited documents.

SUMMARY

Embodiments of the disclosure relate to a massive multiple-inputmultiple-output (M-MIMO) wireless distribution system (WDS) and relatedmethods for optimizing the M-MIMO WDS. In one aspect, the M-MIMO WDSincludes a plurality of remote units each deployed at a location andincludes one or more antennas to serve a remote coverage area. Inexamples discussed herein, the location and a number of the antennasassociated with each of the remote units are adapted to a non-uniformclient device density distribution. As such, at least one remote unitcan have a different number of the antennas from at least one otherremote unit in the M-MIMO WDS. In another aspect, a selected systemconfiguration including the location and the number of the antennasassociated with each of the remote units can be determined using aniterative algorithm. The iterative algorithm utilizes aperformance-estimation function to determine the selected systemconfiguration that maximizes a selected system performance indicator(e.g., system capacity) of the M-MIMO WDS. By configuring the remoteunits based on the non-uniform client device density distribution anddetermining the selected system configuration using the iterativealgorithm, it may be possible to optimize the selected systemperformance indicator at reduced complexity and costs, thus helping toenhance user experiences in the M-MIMO WDS.

In this regard, in one aspect, a M-MIMO WDS is provided. The M-MIMO WDSincludes a plurality of remote units each configured to be deployed at alocation to serve a respective remote coverage area. The M-MIMO WDS alsoincludes a central unit communicatively coupled to each of the pluralityof remote units over a communications link among a plurality ofcommunications links. The central unit is configured to encode areceived downlink communications signal to generate a downlink MIMOcommunications signal. The central unit is also configured to distributethe downlink MIMO communications signal over the plurality ofcommunications links to the plurality of remote units. Each of theplurality of remote units comprises one or more antennas configured todistribute the downlink MIMO communications signal to at least oneclient device located in the respective remote coverage area. At leastone remote unit among the plurality of remote units comprises adifferent number of antennas from at least one other remote unit amongthe plurality of remote units.

In another aspect, a method for optimizing a selected performanceindication of a M-MIMO WDS is provided. The method includes generatingan initial system configuration based on at least one initial systemparameter of a M-MIMO WDS. The initial system configuration comprises aplurality of configuration parameter groups corresponding to a pluralityof remote units in the M-MIMO WDS, respectively. The method alsoincludes providing the plurality of configuration parameter groups to aperformance-estimation function configured to estimate the selectedsystem performance indicator of the M-MIMO WDS based on the plurality ofconfiguration parameter groups. The method also includes generating aninitial estimation of the selected system performance indicator by theperformance-estimation function according to the initial systemconfiguration. The method also includes updating one or more selectedconfiguration parameter groups among the plurality of configurationparameter groups to generate at least one updated system configuration.The method also includes providing the plurality of configurationparameter groups comprising the one or more selected configurationparameter groups to the performance-estimation function. The method alsoincludes generating at least one updated estimation of the selectedsystem performance indicator by the performance-estimation functionaccording to the at least one updated system configuration. The methodalso includes determining a selected system configuration between theinitial system configuration and the at least one updated systemconfiguration corresponding to a higher selected system performanceindicator among the initial estimation of the selected systemperformance indicator and the at least one updated estimation of theselected system performance indicator. The method also includesconfiguring the plurality of remote units based on the selected systemconfiguration.

In another aspect, non-transitory computer-readable medium includingsoftware with instructions is provided. The non-transitorycomputer-readable medium including software with instructions cangenerate an initial system configuration based on at least one initialsystem parameter of a M-MIMO WDS. The initial system configurationcomprises a plurality of configuration parameter groups corresponding toa plurality of remote units in the M-MIMO WDS, respectively. Thenon-transitory computer-readable medium including software withinstructions can also provide the plurality of configuration parametergroups to a performance-estimation function configured to estimate aselected system performance indicator of the M-MIMO WDS based on theplurality of configuration parameter groups. The non-transitorycomputer-readable medium including software with instructions can alsogenerate an initial estimation of the selected system performanceindicator by the performance-estimation function according to theinitial system configuration. The non-transitory computer-readablemedium including software with instructions can also update one or moreselected configuration parameter groups among the plurality ofconfiguration parameter groups to generate at least one updated systemconfiguration. The non-transitory computer-readable medium includingsoftware with instructions can also provide the plurality ofconfiguration parameter groups comprising the one or more selectedconfiguration parameter groups to the performance-estimation function.The non-transitory computer-readable medium including software withinstructions can also generate at least one updated estimation of theselected system performance indicator by the performance-estimationfunction according to the at least one updated system configuration. Thenon-transitory computer-readable medium including software withinstructions can also determine a selected system configuration betweenthe initial system configuration and the at least one updated systemconfiguration corresponding to a higher selected system performanceindicator among the initial estimation of the selected systemperformance indicator and the at least one updated estimation of theselected system performance indicator.

Additional features and advantages will be set forth in the detaileddescription which follows and, in part, will be readily apparent tothose skilled in the art from the description or recognized bypracticing the embodiments as described in the written description andclaims hereof, as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are merely exemplary and are intendedto provide an overview or framework to understand the nature andcharacter of the claims.

The accompanying drawings are included to provide a furtherunderstanding of the disclosure, and are incorporated in and constitutea part of this specification. The drawings illustrate one or moreembodiments, and together with the description serve to explainprinciples and operation of the various embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary wireless distributionsystem (WDS), which may be a distributed antenna system (DAS) forexample;

FIG. 2A is a schematic diagram of an exemplary conventional fullydistributed (FD) massive multiple-input multiple-output (M-MIMO)(FD-M-MIMO) system;

FIG. 2B is a schematic diagram of an exemplary conventional clusterdistributed (CD) M-MIMO (CD-M-MIMO) system that can help reducecomplexity and costs of the conventional FD-M-MIMO system of FIG. 2A;

FIG. 3 is a schematic diagram of an exemplary M-MIMO WDS configured tosupport a plurality of client devices distributed non-uniformlythroughout a coverage area of the M-MIMO WDS;

FIG. 4 is a flowchart illustrating an exemplary process for optimizing aselected performance indicator of the M-MIMO WDS of FIG. 3 based on anon-uniform client device density distribution;

FIG. 5 is a schematic diagram of an exemplary computer system includingone or more non-transitory computer-readable media for storing softwareinstructions to implement a performance-estimation function foroptimizing the selected performance indicator of FIG. 4;

FIG. 6 is flowchart of an exemplary iterative numerical process that canbe employed by the computer system of FIG. 5 to iteratively evaluate aperformance-estimation function to determine a selected systemconfiguration for maximizing system capacity of the M-MIMO WDS of FIG.3;

FIGS. 7A and 7B are plots providing an exemplary network capabilitycomparison between the M-MIMO WDS of FIG. 3, the conventional CD-M-MIMOsystem of FIG. 2B, and a conventional DAS;

FIG. 8 is a schematic diagram of an exemplary WDS provided in the formof an optical fiber-based WDS that can be configured as the M-MIMO WDSof FIG. 3 to support the plurality of client devices distributednon-uniformly throughout the coverage area of the M-MIMO WDS; and

FIG. 9 is a partial schematic cut-away diagram of an exemplary buildinginfrastructure in which a WDS, such as the WDS of FIG. 8, including aplurality of remote units configured and deployed based on a selectedsystem configuration determined via the process of FIG. 4 to maximize aselected system performance indicator (e.g., network capacity) of theWDS.

DETAILED DESCRIPTION

Embodiments of the disclosure relate to a massive multiple-inputmultiple-output (M-MIMO) wireless distribution system (WDS) and relatedmethods for optimizing the M-MIMO WDS. In one aspect, the M-MIMO WDSincludes a plurality of remote units each deployed at a location andincludes one or more antennas to serve a remote coverage area. Inexamples discussed herein, the location and a number of the antennasassociated with each of the remote units are adapted to a non-uniformclient device density distribution. As such, at least one remote unitcan have a different number of the antennas from at least one otherremote unit in the M-MIMO WDS. In another aspect, a selected systemconfiguration including the location and the number of the antennasassociated with each of the remote units can be determined using aniterative algorithm. The iterative algorithm utilizes aperformance-estimation function to determine the selected systemconfiguration that maximizes a selected system performance indicator(e.g., system capacity) of the M-MIMO WDS. By configuring the remoteunits based on the non-uniform client device density distribution anddetermining the selected system configuration using the iterativealgorithm, it may be possible to optimize the selected systemperformance indicator at reduced complexity and costs, thus helping toenhance user experiences in the M-MIMO WDS.

Before discussing exemplary aspects of a M-MIMO WDS and related methodsfor optimizing performance of the M-MIMO WDS, a brief discussion onM-MIMO technology and conventional M-MIMO system topology are firstprovided with reference to FIGS. 2A-2B. The discussion of specificexemplary aspects of a M-MIMO WDS and related methods for optimizingperformance of the M-MIMO WDS starts below with reference to FIG. 3.

M-MIMO is an emerging antenna technology developed by the wirelesscommunications industry as one of the key enabling technologies for theupcoming fifth-generation (5G) wireless communications systems. In aM-MIMO-based communications system, one or more antenna arrays includehundreds or even thousands of antennas employed to simultaneouslycommunicate with a large number of client devices, such as smart phones,laptops, etc., using the same radio frequency (RF) spectral resource. AnM-MIMO antenna system relies on spatial multiplexing for communicatingwith the large number of client devices. By coherently pre-coding awireless communications signal over the antenna array(s), it is possibleto form and direct multiple RF beams toward multiple client devicessimultaneously. Depending on an actual bandwidth requirement of aselected client device among the large number of client devices, anappropriate number of the antennas in the antenna array(s) may be usedto form and transmit an RF beam towards the selected client device. Theselected client device, on the other hand, does not need to employ amatching number of antennas for receiving the RF beam directed to it. Inthis regard, the M-MIMO antenna system can overcome the scalabilitylimitations associated with existing multi-user MIMO (MU-MIMO) antennasystems, thus significantly improving spectral efficiency of the 5Gwireless communications systems to provide increased capacity andenhanced user experiences.

The M-MIMO antenna system may be incorporated into a WDS to support 5Gwireless communications in an indoor environment. However, deployment ofco-located large-scale M-MIMO antenna systems in the indoor environmentbased on a centralized architecture presents several challenges,including the large antenna array form factor (at frequencies below 6gigahertz (GHz)) and lower performance due to increased signal lossescoming from building walls. As such, distributed M-MIMO antennaarchitectures become more preferable in the indoor environment due tocost and performance benefits over the centralized architecture.

In this regard, FIG. 2A is a schematic diagram of an exemplaryconventional fully distributed (FD) M-MIMO (FD-M-MIMO) system 200. Theconventional FD-M-MIMO system 200 includes a plurality of remote units202 coupled to a central unit 204 via a plurality of communication links206, respectively. The remote units 202 include a plurality of antennas208, respectively. The remote units 202 may be geographicallydistributed within a coverage boundary of a wireless cell 210 based on apredefined user distribution profile. In this regard, the antennas 208in the remote units 202 collectively form an M-MIMO antenna array forsimultaneously distributing multiple data streams at the same RFfrequency to multiple client devices 212 located within the coverageboundary of the wireless cell 210.

The conventional FD-M-MIMO system 200 can provide higher networkcapacity, since the antennas 208 can transmit the multiple data streamsat the same RF frequency at the same time. However, since each of theremote units 202 is connected to the central unit 204 by a dedicatedcommunication link 206, system complexity as well as hardware andinstallation costs of the conventional FD-M-MIMO system 200 may increasesignificantly. Thus, an alternative M-MIMO system architecture has beendeveloped by the industry to help reduce the complexity and costs of theconventional FD-M-MIMO system 200.

In this regard, FIG. 2B is a schematic diagram of an exemplaryconventional cluster distributed (CD) M-MIMO (CD-M-MIMO) system 214 thatcan help reduce the complexity and costs of the conventional FD-M-MIMOsystem 200 of FIG. 2A. Common elements between FIGS. 2A and 2B are showntherein with common element numbers and will not be re-described herein.

The conventional CD-M-MIMO system 214 includes a plurality of remoteunits 216 coupled to a central unit 218 via a plurality of communicationlinks 220, respectively. The remote units 216 may be geographicallydistributed within the coverage boundary of the wireless cell 210 basedon the predefined user distribution profile. The spatial distribution ofthe remote units 216 is designed such that good coverage of the wirelesscell 210 is obtained. In contrast to the remote units 202 in theconventional FD-M-MIMO system 200 of FIG. 2A, each of the remote units216 includes a plurality of antennas 222. A number of the antennas 222included in each of the remote units 216 is configured to be the same.For example, as shown in FIG. 2B, each of the remote units 216 includessix antennas. Similar to the conventional FD-M-MIMO system 200 of FIG.2A, the antennas 222 in each of the remote units 216 collectively forman M-MIMO antenna array for simultaneously distributing multiple datastreams at the same RF frequency to the multiple client devices 212located within the coverage boundary of the wireless cell 210. Theconventional CD-M-MIMO system 214 has a lower system complexity and asmaller installation cost compared to the conventional FD-M-MIMO system200 of FIG. 2A as the number of the remote units 216 and thecommunication links 220 are reduced.

As discussed above, the remote units 202 in the conventional FD-M-MIMOsystem 200 and the remote units 216 in the conventional CD-M-MIMO system214 are geographically distributed within the coverage boundary of thewireless cell 210 based on the predefined user distribution profile.Often time, the predefined user distribution profile assumes uniformdistribution of the multiple client devices 212 throughout the wirelesscell 210. However, the multiple client devices 212 are more likely to bedistributed non-uniformly in most indoor environments. For example, a UEdensity in a conference room can reach three persons per square-meter (3persons/m²). In contrast, the UE density in a cubical area may be just0.15 persons/m². Experiments have shown that the average UE density in atypical office building is approximately 0.05 persons/m². In thisregard, the complexity of non-uniform UE distribution in the indoorenvironment necessitates advanced M-MIMO system architecture adapted toeffectively utilize network infrastructure and RF spectral resourcesbased on non-uniform distribution of the multiple client devices 212.

In this regard, FIG. 3 is a schematic diagram of an exemplary M-MIMO WDS300 configured to support a plurality of client devices 302 distributednon-uniformly throughout a coverage area 304 of the M-MIMO WDS 300. Asis further discussed below in FIG. 3, the M-MIMO WDS 300 differs fromthe conventional FD-M-MIMO system 200 of FIG. 2A and the conventionalCD-M-MIMO system 214 of FIG. 2B in two aspects. First, the M-MIMO WDS300 includes a plurality of remote units 306(1)-306(N) to which radioresources (e.g., antennas) are strategically allocated to maximize aselected system performance indicator (e.g., network capacity) based ona non-uniform client device density distribution. Second, geographiclocations (e.g., location coordinates) of the remote units 306(1)-306(N)are determined according to the non-uniform client device densitydistribution and adapted to an indoor wireless signal propagationenvironment. In this regard, the M-MIMO WDS 300 is configured based on anon-uniform distributed (ND) M-MIMO (ND-M-MIMO) architecture. Further,as discussed later with reference to FIG. 4, an iterative algorithmemploying a performance-related objective function can be utilized todetermine radio resource allocations and geographic locations for theremote units 306(1)-306(N) in the M-MIMO WDS 300.

The M-MIMO WDS 300 configured based on the ND-M-MIMO architecture isadvantageous over the conventional FD-M-MIMO system 200 of FIG. 2A andthe conventional CD-M-MIMO system 214 of FIG. 2B in a variety ofaspects. First, the M-MIMO WDS 300 can be implemented with a significantreduction in network infrastructure and installation cost. Second, theM-MIMO WDS 300 can significantly improve network capacity at minimalcost. Third, the M-MIMO WDS 300 provides greater architecturalflexibility and scalability, thus making it possible to adapt the M-MIMOWDS 300 to support future wireless technologies (e.g. 5G technology) atminimal cost. For example, given that the M-MIMO WDS 300 is agnostic tooperating RF carrier frequency, the M-MIMO WDS 300 can be adapted tosupport millimeter-wave 5G networks at minimal cost. In addition, it maybe possible to further increase system capacity and/or reliability ofthe M-MIMO WDS 300 by adding more antennas to the remote units306(1)-306(N). Furthermore, the M-MIMO WDS 300 can support all forms ofbase station functional splits and is also agnostic to front-haul andmid-haul transmission technologies (e.g., fiber optical-basedtransmission technology).

With continuing reference to FIG. 3, the remote units 306(1)-306(N) areconfigured to be deployed at a plurality of locations(x₁,y₁)-(x_(N),y_(N)) to serve a plurality of remote coverage areas308(1)-308(N), respectively. The respective location for each of theremote units 306(1)-306(N) is represented by a pair of coordinates(x_(i),y_(i)) (1≤i≤N). In a non-limiting example, the pair ofcoordinates (x_(i),y_(i)) can correspond to a pair of longitude-latitudecoordinates as determined by such system as the Global PositioningSystem (GPS). In another non-limiting example, the pair of coordinates(x_(i),y_(i)) can be represented by a pair of Cartesian coordinates thatare arbitrarily determined based on a layout map of the coverage area304. It should be appreciated other coordinate systems (e.g., Polarcoordinate system) may also be used to represent the pair of coordinates(x_(i),y_(i)) for each of the remote units 306(1)-306(N).

The M-MIMO WDS 300 includes a central unit 310 communicatively coupledto the remote units 306(1)-306(N) over a plurality of communicationslinks 312(1)-312(N), respectively. The communications links312(1)-312(N) may be fiber optical-based communications links or anyother type of communications links. The central unit 310 is configuredto encode a received downlink communications signal 314 to generate adownlink MIMO communications signal 316 and provide the downlink MIMOcommunications signal 316 to the remote units 306(1)-306(N) over thecommunications links 312(1)-312(N). The central unit 310 may employ abaseband unit to process (e.g., pre-code) the downlink MIMOcommunications signal 316 prior to distributing the downlink MIMOcommunications signal 316 to the remote units 306(1)-306(N). In thisregard, the remote units 306(1)-306(N) in the M-MIMO WDS 300 areconfigured to simultaneously transmit the downlink MIMO communicationssignal 316 in the same RF spectrum (e.g., channel or band).

Each of the remote units 306(1)-306(N) includes one or more antennas318(1)-318(M) configured to distribute the downlink MIMO communicationssignal 316 to at least one of the client devices 302 located in arespective coverage area among the remote coverage areas 308(1)-308(N).In a non-limiting example, a remote unit located in any of the remotecoverage areas 308(1)-308(N) can transmit the downlink MIMOcommunications signal 316 concurrently from a subset of the antennas318(1)-318(M) (e.g., two, three, or four) to a respective client deviceamong the client devices 302 if the respective client device is equippedwith an equal number (e.g., two, three, or four) of antennas. In thisregard, the remote unit is transmitting the downlink MIMO communicationssignal 316 via MIMO. Alternatively, the remote unit located in any ofthe remote coverage areas 308(1)-308(N) can utilize the subset of theantennas 318(1)-318(M) (e.g., two, three, or four) to form an RF beamfor distributing the downlink MIMO communications signal 316 to therespective client device among the client devices 302 if the respectiveclient device is not equipped with an equal number of antennas. In thisregard, the remote unit is transmitting the downlink MIMO communicationssignal 316 via RF beamforming. By transmitting the downlink MIMOcommunications signal 316 using MIMO and/or RF beamforming, the remoteunits 306(1)-306(N) in the M-MIMO WDS 300 can adapt flexibly to thereceiving capabilities of the client devices 302. As a result, theclient devices 302 may be able to receive the downlink MIMOcommunications signal 316 in any of the remote coverage areas308(1)-308(N) with a desired RF signal quality (e.g., signal-to-noiseratio (SNR)) and data throughput.

In addition to distributing the downlink MIMO communications signal 316to the client devices 302, each of the remote units 306(1)-306(N) isconfigured to receive at least one uplink communications signal 320 fromat least one of the client devices 302 and provide the received uplinkcommunications signal 320 to the central unit 310 over a respectivecommunications link among the communications links 312(1)-312(N). Thecentral unit 310 may combine the uplink communications signal 320received from the remote units 306(1)-306(N) and/or perform additionalsignal processing (e.g., filtering, frequency conversion, and signalconversion).

As mentioned earlier, the M-MIMO WDS 300 is configured based on theND-M-MIMO architecture adapted to the non-uniform client device densitydistribution throughout the coverage area 304. In a non-limitingexample, remote coverage area 308(1) (e.g., a conference room) has ahigher client device density than remote coverage area 308(2) (e.g., ahallway). In the same non-limiting example, the remote coverage area308(2) has a higher client device density than remote coverage area308(N) (e.g., a cubical area). Accordingly, the demand for datathroughput in the remote coverage area 308(1) would be higher than thatin the remote coverage area 308(2). Similarly, the demand for datathroughput in the remote coverage area 308(2) would be higher than thatin the remote coverage area 308(N). In this regard, to optimize userexperience in the M-MIMO WDS 300, it may be necessary to maximize systemcapacity of the M-MIMO WDS 300 throughout the coverage area 304. Inexamples discussed hereinafter, the system capacity of the M-MIMO WDS300 refers to an aggregated data throughput in the remote coverage areas308(1)-308(N).

In one aspect, the locations (x₁,y₁)-(x_(N),y_(N)) of the remote units306(1)-306(N) are strategically determined based on the client devicedensity distribution throughout the coverage area 304. For example, thelocation (x₁,y₁) of remote unit 306(1) can correspond to a center pointof the remote coverage are 308(1). In another aspect, given that theremote coverage area 308(1) has a higher client device density than theremote coverage area 308(2), the remote unit 306(1) can be configured toinclude more antennas than remote unit 306(2). Likewise, since theremote coverage area 308(2) has a higher client device density than theremote coverage area 308(N), the remote unit 306(2) can be configured toinclude more antennas than remote unit 306(N). In this regard, given thenon-uniform client device density distribution in the coverage area 304,at least one remote among the remote units 306(1)-306(N) (e.g., theremote unit 306(1)) is configured to include a different number ofantennas from at least one other remote unit among the remote units306(1)-306(N) (e.g., the remote unit 306(2)). By strategicallydetermining the locations (x₁,y₁)-(x_(N),y_(N)) of the remote units306(1)-306(N) and allocating a respective number of the antennas318(1)-318(N) to each of the remote units 306(1)-306(N) based on thenon-uniform client device density distribution, it may be possible tomaximize the system capacity of the M-MIMO WDS 300 at reduced complexityand cost, while preserving flexibility and scalability for supportingfuture RF spectrums and/or communications technologies.

The locations (x₁,y₁)-(x_(N),y_(N)) of the remote units 306(1)-306(N)and the respective number of the antennas 318(1)-318(N) allocated toeach of the remote units 306(1)-306(N) can be determined systematicallybased on a process. In this regard, FIG. 4 is a flowchart illustratingan exemplary process 400 for optimizing a selected performance indicatorof the M-MIMO WDS 300 of FIG. 3 based on the non-uniform client devicedensity distribution.

With reference to FIG. 4, the process 400 includes generating an initialsystem configuration based on at least one initial system parameter ofthe M-MIMO WDS 300 (block 402). The initial system configurationincludes a plurality of configuration parameter groups P₁-P_(N)corresponding to the remote units 306(1)-306(N), respectively. In anon-limiting example, a configuration parameter group Pi (1≤i≤N) amongthe configuration parameter groups P₁-P_(N) includes configurationparameters (x_(i), y_(i), n_(i)) (1≤i≤N). Among the configurationparameters, (x_(i), y_(i)) represents respective location coordinates ofa remote unit 306(i) and n_(i) represents a respective number ofantennas provided in the remote unit 306(i) (1≤i≤N). Accordingly, theconfiguration parameter groups P₁-P_(N) may be expressed as (x₁, y₁,n₁)-(x_(N), y_(N), n_(N)) for the remote units 306(1)-306(N),respectively. In another non-limiting example, the initial systemparameter used to generate the initial system configuration includessuch parameters of the M-MIMO WDS 300 as total number of antennas, totalnumber of remote units, system layout, client device densitydistribution, and/or coverage area RF survey.

Next, the configuration parameter groups P₁-P_(N) are provided to aperformance-estimation function ƒ (P₁-P_(N)), which is configured toestimate the selected system performance indicator of the M-MIMO WDS 300based on the configuration parameter groups P₁-P_(N) (block 404). Theperformance-estimation function ƒ (P₁-P_(N)), which may be implementedby a computing device (e.g., a personal computer, a laptop, etc.) basedon software instructions stored in a non-transitory computer-readablemedium, will be further discussed later with reference to FIG. 5. Theperformance-estimation function ƒ (P₁-P_(N)) generates an initialestimation of the selected system performance indicator according to theinitial system configuration (block 406).

Subsequently, one or more selected configuration parameter groups amongthe configuration parameter groups P₁-P_(N) are updated to generate atleast one updated system configuration (block 408). As will be furtherdiscussed later with reference to FIG. 5, the selected configurationparameter groups among the configuration parameter groups P₁-P_(N) maybe changed based on predetermined iteration steps. The configurationparameter groups P₁-P_(N), which now include the updated selectedconfiguration parameter groups, are provided to theperformance-estimation function ƒ (P₁-P_(N)) (block 410) Theperformance-estimation function ƒ (P₁-P_(N)) generates at least oneupdated estimation of the selected system performance indicatoraccording to the updated system configuration (block 412).

Next, a selected system configuration between the initial systemconfiguration and the updated system configuration can be determined(block 414). The selected system configuration corresponds to a higherselected system performance indicator between the initial estimation ofthe selected system performance indicator and the updated estimation ofthe selected system performance indicator determined by theperformance-estimation function ƒ (P₁-P_(N)). Then, it is possible toconfigure the remote units 306(1)-306(N) based on the selected systemconfiguration, thus maximizing the selected system performance indicatorin the M-MIMO WDS 300 (block 416). The selected system configuration maybe output to, for example a printer, a computer monitor, a storagemedia, etc., prior to configuring the remote units 306(1)-306(N) in theM-MIMO WDS 300.

In a non-limiting example, the selected system performance indicatorrefers to the system capacity of the M-MIMO WDS 300. In this regard, theprocess 400 can be utilized to optimize the system capacity of theM-MIMO WDS 300. Accordingly, in block 404, the configuration parametergroups P₁-P_(N) are provided to the performance-estimation function ƒ(P₁-P_(N)), which is configured to estimate the system capacity of theM-MIMO WDS 300 based on the configuration parameter groups P₁-P_(N). Inblock 406, the performance-estimation function ƒ (P₁-P_(N)) generates aninitial system capacity according to the initial system configuration.In block 408, one or more selected configuration parameter groups amongthe configuration parameter groups P₁-P_(N) are updated to generate atleast one updated system configuration. In block 410, the configurationparameter groups P₁-P_(N), which now include the updated selectedconfiguration parameter groups, are provided to theperformance-estimation function ƒ (P₁-P_(N)). In block 412, theperformance-estimation function ƒ (P₁-P_(N)) generates at least oneupdated system capacity according to the updated system configuration.In block 414, the selected system configuration between the initialsystem configuration and the updated system configuration can bedetermined. The selected system configuration corresponds to a highersystem capacity between the initial system capacity and the updatedsystem capacity determined by the performance-estimation function ƒ(P₁-P_(N)). In block 416, the remote units 306(1)-306(N) are configuredbased on the selected system configuration to maximize the systemcapacity in the M-MIMO WDS 300.

As mentioned earlier, a computing device (e.g., a personal computer, alaptop, etc.) may implement the performance-estimation function ƒ(P₁-P_(N)) based on software instructions stored in a non-transitorycomputer-readable medium. In this regard, FIG. 5 is a schematic diagramof an exemplary computer system 500 including one or more non-transitorycomputer-readable media 502(1)-502(4) for storing software instructionsto implement the performance-estimation function ƒ (P₁-P_(N)) of FIG. 4.Common elements between FIGS. 3, 4, and 5 are shown therein with commonelement numbers and will not be re-described herein.

With reference to FIG. 5, the non-transitory computer-readable media502(1)-502(4) further include a hard drive 502(1), an on-board memorysystem 502(2), a compact disc 502(3), and a floppy disk 502(4). Each ofthe non-transitory computer-readable media 502(1)-502(4) may beconfigured to store the software instructions to implement theperformance-estimation function ƒ (P₁-P_(N)). The computer system 500also includes a keyboard 504 and a computer mouse 506 for inputting thesoftware instructions onto the non-transitory computer-readable media502(1)-502(4). The keyboard 504 and the computer mouse 506 may also beused to input the initial system parameter of the M-MIMO WDS 300, whichcan be used to generate the initial system configuration in block 402 ofFIG. 4. The computer system 500 also includes a monitor 508 foroutputting the selected system configuration for configuring the remoteunits 306(1)-306(N). Further, the computer system 500 includes aprocessor 510 configured to read the software instructions from thenon-transitory computer-readable media 502(1)-502(4) and execute thesoftware instructions to implement the performance-estimation function ƒ(P₁-P_(N)). While the computer system 500 is illustrated as a singledevice, the computer system 500 may also be a computer network deployedaccording to a centralized topology or a distributed topology.

When the process 400 of FIG. 4 is utilized to optimize the systemcapacity of the M-MIMO WDS 300, the performance-estimation function ƒ(P₁-P_(N)) can be used to calculate an average data rate of a clientdevice over the coverage area 304 for a given configuration parametergroup of the configuration parameter groups P₁-P_(N). Theperformance-estimation function ƒ (P₁-P_(N)) is a function of theconfiguration parameter groups P₁-P_(N) and can be expressed as equationEq. 1 below.

$\begin{matrix}{{f\left( {P_{1} - P_{N}} \right)} = {\frac{B}{\int{\int{{W\left( {x,y} \right)}{dxdy}}}}{\int{\int{{\log_{2}\left\lbrack {1 + {\left( \frac{\lambda}{4\; \pi} \right)^{r}\frac{P_{s}}{P_{n}}{\sum\limits_{k = 1}^{N}\frac{n_{k}^{q}}{d_{k}^{r}\left( {x,y,x_{k},y_{k}} \right)}}}} \right\rbrack}{W\left( {x,y} \right)}{dxdy}}}}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

In the equation Eq. 1 above, B represents a channel bandwidth of thedownlink MIMO communications signal 316 to be distributed by the remoteunits 306(1)-306(N) in the coverage area 304. For example, the channelbandwidth of the downlink MIMO communications signal 316 can be 20megahertz (MHz) in such wireless communications systems as long-termevolution (LTE). W(x,y) is a weighting function representing suchspatially-dependent parameters as system layout information, clientdevice density distribution, and signal propagation environment atselected locations in the coverage area 304 of the M-MIMO WDS 300.According to the non-limiting example discussed earlier with referenceto FIG. 3, the remote coverage area 308(1) (e.g., a conference room) hasa higher client device density than the remote coverage area 308(2)(e.g., a hallway), and the remote coverage area 308(2) has a higherclient device density than the remote coverage area 308(N) (e.g., acubical area). In this regard, the remote coverage area 308(1) would beassigned a higher weighting factor than the remote coverage area 308(2),and the remote coverage area 308(2) would be assigned a higher weightingfactor that the remote coverage area 308(N). Accordingly, the remotecoverage area 308(1) would correspond to a higher weight than the remotecoverage area 308(2) in the weighting function W(x,y) and the remotecoverage area 308(2) would correspond to a higher weight than the remotecoverage area 308(N) in the weighting function W(x,y). In this regard,the performance-estimation function ƒ (P₁-P_(N)) is proportionallyrelated to the weighting function W(x,y). The double integration∫∫W(x,y)dxdy represents a normalized weighting function (e.g., totalweight) throughout the coverage area 304.

With continuing reference to the equation Eq. 1, P_(s) and P_(n)represent signal power and noise power in the coverage area 304,respectively. d_(k) ^(r)(x, y, x_(k), y_(k)) represents a distancebetween the location (x_(k), y_(k)) of the remote unit 306(K) (1≤k≤N)and a selected location (x,y) in the coverage area 304. r represents anexponential of a free-space propagation attenuation model. n_(k) ^(q)represents a number of antennas in the remote unit 306(K) (1≤k≤N),wherein q represents expected signal coherency between multiple copiesof the downlink MIMO communications signal 316 received by one of theclient devices 302 at the location (x,y). In a non-limiting example, qis a positive decimal number between 1 (inclusive) and 2 (inclusive),with 1 representing the least coherency and 2 representing the highestcoherency. Notably, the parameters B, P_(s), P_(n), r, and q arepredetermined and fixed for each execution of the process 400.

Given that the performance-estimation function ƒ (P₁-P_(N)) is dependentof the configuration parameter groups P₁-P_(N), a change to any of theconfiguration parameter groups P₁-P_(N) may result in a significantchange in the result of the performance-estimation function ƒ (P₁-P_(N))and consequently the system capacity of the M-MIMO WDS 300. Notably,there exists a vast number of possible combinations of the configurationparameter groups P₁-P_(N). As such, to determine a selectedconfiguration parameter group of the configuration parameter groupsP₁-P_(N) that can maximize the system capacity of the M-MIMO WDS 300, itmay be necessary to evaluate the vast number of possible combinations ofthe configuration parameter groups P₁-P_(N). The computer system 500 maybe configured via the software instructions stored in the non-transitorycomputer-readable media 502(1)-502(4) to help determine the selectedconfiguration parameter group of the configuration parameter groupsP₁-P_(N) for maximizing the system capacity of the M-MIMO WDS 300. Morespecifically, the software instructions may be so programmed toiteratively test the performance-estimation function ƒ (P₁-P_(N)) basedon strategically selected combinations of the configuration parametergroups P₁-P_(N) among the vast number of possible combinations of theconfiguration parameter groups P₁-P_(N). The performance-estimationfunction ƒ (P₁-P_(N)) may converge quickly (e.g., in one to two minutesfor the coverage area 304 of approximately 1200 square meters).

FIG. 6 is flowchart of an exemplary iterative numerical process 600 thatcan be employed by the computer system 500 of FIG. 5 to iterativelyevaluate the performance-estimation function ƒ (P₁-P_(N)) of FIG. 4 todetermine the selected system configuration for maximizing the systemcapacity of the M-MIMO WDS 300 of FIG. 3. According to the iterativenumerical process 600, one or more initial system parameters (e.g.,total number of antennas, total number of remote units, system layout,client device density distribution, and/or coverage area RF survey ofthe M-MIMO WDS 300) are input into the computer system 500 (block 602).Next, the computer system 500 generates a random initial systemconfiguration including the configuration parameter groups P₁-P_(N)(block 604). Next, the computer system 500 estimates the system capacitycorresponding to the random initial system configuration using theperformance-estimation function ƒ (P₁-P_(N)) (block 606). The computersystem 500 may store the system capacity corresponding to the randominitial system configuration in a first programmable variable VAR₁.Next, the computer system 500 updates one or more selected configurationparameter groups among the configuration parameter groups P₁-P_(N) togenerate an updated system configuration (block 608). In a non-limitingexample, the selected configuration parameter group can be updated asshown below.

${x_{k}^{({i + 1})} = {x_{k}^{(i)} + {\Delta \; x\frac{\partial f}{\partial x_{k}}}}},\mspace{14mu} {y_{k}^{({i + 1})} = {y_{k}^{(i)} + {\Delta \; y\frac{\partial f}{\partial y_{k}}}}},\mspace{14mu} {n_{k}^{({i + 1})} = {n_{k}^{(i)} + {\Delta \; n\frac{\partial f}{\partial n_{k}}}}}$

In the expression above, (x_(k) ^((i)), y_(k) ^((i)), n_(k) ^((i))) arethe location and antenna count of remote unit 306(K) (1≤k≤N) of FIG. 3in an i-th iteration of the iterative numerical process 600. Similarly,(x_(k) ^((i+1)), y_(k) ^((i+1)), n_(k) ^((i+1))) are the updatedlocation and antenna count of the remote unit 306(K) for an (i+1)-thiteration of the iterative numerical process 600. Parameters Δx, Δy, Δnrepresent iteration step sizes between the i-th iteration and the(i+1)-th iteration.

The computer system 500 then estimates the system capacity correspondingto the updated system configuration using the performance-estimationfunction ƒ (P₁-P_(N)) (block 610). The computer system 500 may store thesystem capacity corresponding to the updated system configuration in asecond programmable variable VAR₂. The computer system 500 then checksto see if the system capacity nears saturation (block 612). In thisregard, the computer system 500 may compare the second programmablevariable VAR₂ against the first programmable variable VAR₁ to determinewhether the second programmable variable VAR₂ is higher than the firstprogrammable variable VAR₁.

If the second programmable variable VAR₂ is higher than the firstprogrammable variable VAR₁ by a predefined threshold, the computersystem 500 may conclude that the updated system configuration can leadto a better system capacity. Accordingly, the computer system 500 maycopy the second programmable variable VAR₂ to the first programmablevariable VAR₁ and return to block 608 for the next iteration.

If the second programmable variable VAR₂ is higher than the firstprogrammable variable VAR₁ by less than the predefined threshold, thecomputer system 500 may conclude that the system capacity has beenmaximized. Accordingly, the computer system 500 may output theoptimization results stored in the second programmable variable VAR₂(block 614). The computer system 500 then concludes the iterativenumerical process 600.

However, if the second programmable variable VAR₂ is lower than thefirst programmable variable VAR₁, the computer system 500 may concludethat the updated system configuration does not lead to a better systemcapacity. Accordingly, the computer system 500 may discard the updatedsystem configuration, reset the second programmable variable VAR₂ tozero, and return to block 608 for the next iteration.

Using the iterative numerical process 600, it may be possible to quicklydetermine the selected system configuration including an optimalcombination of the configuration parameter groups P₁-P_(N) to maximizethe system capacity of the M-MIMO WDS 300. It should be appreciated thatthe iterative numerical process 600 is not limited to maximizing thesystem capacity of the M-MIMO WDS 300. By making necessary adjustmentsto the performance-estimation function ƒ (P₁-P_(N)), the iterativenumerical process 600 may be used to optimize any selected systemperformance indicator of the M-MIMO WDS 300.

The M-MIMO WDS 300 of FIG. 3 optimized based on the process 400 of FIG.4 and/or the iterative numerical process 600 of FIG. 6 can providehigher system capacity over a conventional distributed antenna system(DAS) and the conventional CD-M-MIMO system 214 of FIG. 2B. In thisregard, FIGS. 7A and 7B are plots providing an exemplary networkcapability comparison between the M-MIMO WDS 300 of FIG. 3, theconventional CD-M-MIMO system 214 of FIG. 2B, and a conventional DAS.

In a non-limiting example, the network capacity comparisons asillustrated in FIGS. 7A and 7B are based on a total of twelve antennasprovided in each of the systems. The conventional DAS includes sixremote units each having two antennas. The conventional CD-M-MIMO system214 includes three remote units each having four antennas. The M-MIMOWDS 300 includes three remote units having two, five, and five antennas,respectively. Notably, according to previous discussions with referenceto FIG. 3, the two remote units with five antennas are located in remotecoverage areas with higher client device densities, while the remoteunit with two antennas is located in a remote coverage area with a lowerclient device density.

FIG. 7A includes a first capacity-probability curve 702, a secondcapacity-probability curve 704, and a third capacity-probability curve706. The first capacity-probability curve 702 illustrates a networkcapability of the conventional DAS at various probabilities. The secondcapacity-probability curve 704 illustrates a network capability of theconventional CD-M-MIMO system 214 at various probabilities. The thirdcapacity-probability curve 706 illustrates a network capability of theM-MIMO WDS 300 at various probabilities. As shown in FIG. 7A, at fiftypercent (50%) probability, the network capacity of the M-MIMO WDS 300 ishigher than the network capacity of the conventional CD-M-MIMO system214 by 85 megabits per second (Mbps).

FIG. 7B includes a first bar graph 708, a second bar graph 710, and athird bar graph 712 corresponding to the conventional DAS, theconventional CD-M-MIMO system 214, and the M-MIMO WDS 300, respectively.FIG. 7B shows that an average cell edge data rate of the M-MIMO WDS 300is higher than an average cell edge data rate of the conventionalCD-M-MIMO system 214 by 27 Mbps. FIG. 7B also shows that an average datarate of the M-MIMO WDS 300 is higher than an average data rate of theconventional CD-M-MIMO system 214 by 85 Mbps. FIG. 7B further shows thatan average peak data rate of the M-MIMO WDS 300 is higher than anaverage peak data rate of the conventional CD-M-MIMO system 214 by 215Mbps. In summary, the M-MIMO WDS 300 can bring an approximately 37%capacity gain as compared to the conventional CD-M-MIMO system 214.Further, the M-MIMO WDS 300 can bring an approximately 2.3 times highercapacity than the conventional DAS, even with fewer remote units.

FIG. 8 is a schematic diagram of an exemplary WDS 800 provided in theform of an optical fiber-based WDS that can be configured as the M-MIMOWDS 300 of FIG. 3 to support the client devices 302 distributednon-uniformly throughout the coverage area 304 of the M-MIMO WDS 300.The WDS 800 includes an optical fiber for distributing communicationsservices for multiple frequency bands. The WDS 800 in this example iscomprised of three main components. A plurality of radio interfacesprovided in the form of radio interface modules (RIMs) 802(1)-802(M) areprovided in a central unit 804 to receive and process one or moredownlink communications signals 806D(1)-806D(R) prior to opticalconversion into downlink optical fiber-based communications signals. Thedownlink communications signals 806D(1)-806D(R) may be received from abase station as an example. The RIMs 802(1)-802(M) provide both downlinkand uplink interfaces for signal processing. The notations “1-R” and“1-M” indicate that any number of the referenced component, 1-R and 1-M,respectively, may be provided. The central unit 804 is configured toaccept the RIMs 802(1)-802(M) as modular components that can easily beinstalled and removed or replaced in the central unit 804. In oneexample, the central unit 804 is configured to support up to twelve RIMs802(1)-802(12). Each of the RIMs 802(1)-802(M) can be designed tosupport a particular type of radio source or range of radio sources(i.e., frequencies) to provide flexibility in configuring the centralunit 804 and the WDS 800 to support the desired radio sources.

For example, one RIM 802 may be configured to support the PersonalizedCommunications System (PCS) radio band. Another RIM 802 may beconfigured to support the 800 MHz radio band. In this example, byinclusion of the RIMs 802(1)-802(M), the central unit 804 could beconfigured to support and distribute communications signals on both PCSand Long-Term Evolution (LTE) 700 radio bands, as an example. The RIMs802(1)-802(M) may be provided in the central unit 804 that support anyfrequency bands desired, including, but not limited to, the US Cellularband, PCS band, Advanced Wireless Service (AWS) band, 700 MHz band,Global System for Mobile communications (GSM) 900, GSM 1800, andUniversal Mobile Telecommunications System (UMTS). The RIMs802(1)-802(M) may also be provided in the central unit 804 that supportany wireless technologies desired, including, but not limited to, CodeDivision Multiple Access (CDMA), CDMA200, 1×RTT, Evolution-Data Only(EV-DO), UMTS, High-speed Packet Access (HSPA), GSM, General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), TimeDivision Multiple Access (TDMA), LTE, iDEN, and Cellular Digital PacketData (CDPD).

The RIMs 802(1)-802(M) may be provided in the central unit 804 thatsupport any frequencies desired, including, but not limited to, US FCCand Industry Canada frequencies (824-849 MHz on uplink and 869-894 MHzon downlink), US FCC and Industry Canada frequencies (1850-1915 MHz onuplink and 1930-1995 MHz on downlink), US FCC and Industry Canadafrequencies (1710-1755 MHz on uplink and 2110-2155 MHz on downlink), USFCC frequencies (698-716 MHz and 776-787 MHz on uplink and 728-746 MHzon downlink), EU R & TTE frequencies (880-915 MHz on uplink and 925-960MHz on downlink), EU R & TTE frequencies (1710-1785 MHz on uplink and1805-1880 MHz on downlink), EU R & TTE frequencies (1920-1980 MHz onuplink and 2110-2170 MHz on downlink), US FCC frequencies (806-824 MHzon uplink and 851-869 MHz on downlink), US FCC frequencies (896-901 MHzon uplink and 929-941 MHz on downlink), US FCC frequencies (793-805 MHzon uplink and 763-775 MHz on downlink), and US FCC frequencies(2495-2690 MHz on uplink and downlink).

With continuing reference to FIG. 8, the central unit 804 may convertthe downlink communications signals 806D(1)-806D(R) into a downlink MIMOcommunications signal 807 and provide the downlink MIMO communicationssignal 807 to a plurality of optical interfaces provided in the form ofoptical interface modules (OIMs) 808(1)-808(N) in this embodiment toconvert the downlink MIMO communications signal 807 into a plurality ofdownlink optical fiber-based MIMO communications signals810D(1)-810D(R). The notation “1-N” indicates that any number of thereferenced component 1-N may be provided. The OIMs 808(1)-808(N) may beconfigured to provide a plurality of optical interface components (OICs)that contain optical-to-electrical (O/E) and electrical-to-optical (E/O)converters, as will be described in more detail below. The OIMs808(1)-808(N) support the radio bands that can be provided by the RIMs802(1)-802(M), including the examples previously described above.

The OIMs 808(1)-808(N) each include E/O converters to convert thedownlink MIMO communications signal 807 into the downlink opticalfiber-based MIMO communications signals 810D(1)-810D(R). The downlinkoptical fiber-based MIMO communications signals 810D(1)-810D(R) arecommunicated over a downlink optical fiber-based communications medium812D to a plurality of remote units 814(1)-814(S). The remote units814(1)-814(S) may be configured and deployed based on the selectedsystem configuration determined via the process 400 of FIG. 4 tomaximize the selected system performance indicator (e.g., networkcapacity). The notation “1-S” indicates that any number of thereferenced component 1-S may be provided. Remote unit O/E convertersprovided in the remote units 814(1)-814(S) convert the downlink opticalfiber-based MIMO communications signals 810D(1)-810D(R) back into thedownlink MIMO communications signal 807. The downlink MIMOcommunications signal 807 is provided to antennas 816(1)-816(S) in theremote units 814(1)-814(S) to client devices in the reception range ofthe antennas 816(1)-816(S).

The remote units 814(1)-814(S) receive a plurality of uplinkcommunications signals from the client devices through the antennas816(1)-816(S). Remote unit E/O converters are also provided in theremote units 814(1)-814(S) to convert the uplink communications signals818U(1)-818U(S) into a plurality of uplink optical fiber-basedcommunications signals 810U(1)-810U(S). The remote units 814(1)-814(S)communicate the uplink optical fiber-based communications signals810U(1)-810U(S) over an uplink optical fiber-based communications medium812U to the OIMs 808(1)-808(N) in the central unit 804. The OIMs808(1)-808(N) include O/E converters that convert the received uplinkoptical fiber-based communications signals 810U(1)-810U(S) into aplurality of uplink communications signals 820U(1)-820U(S), which areprocessed by the RIMs 802(1)-802(M) and provided as the uplinkcommunications signals 820U(1)-820U(S). The central unit 804 may providethe uplink communications signals 820U(1)-820U(S) to a base station orother communications system.

Note that the downlink optical fiber-based communications medium 812Dand the uplink optical fiber-based communications medium 812U connectedto each of the remote units 814(1)-814(S) may be a common opticalfiber-based communications medium, wherein for example, wave divisionmultiplexing (WDM) is employed to provide the downlink opticalfiber-based MIMO communications signals 810D(1)-810D(R) and the uplinkoptical fiber-based communications signals 810U(1)-810U(S) on the sameoptical fiber-based communications medium.

The WDS 800 of FIG. 8 may be provided in an indoor environment, asillustrated in FIG. 9. FIG. 9 is a partial schematic cut-away diagram ofan exemplary building infrastructure 900 in which a WDS, such as the WDS800 of FIG. 8, including a plurality of remote units configured anddeployed based on the selected system configuration determined via theprocess 400 of FIG. 4 to maximize the selected system performanceindicator (e.g., network capacity) of the WDS 800. The buildinginfrastructure 900 in this embodiment includes a first (ground) floor902(1), a second floor 902(2), and a third floor 902(3). The floors902(1)-902(3) are serviced by a central unit 904 to provide antennacoverage areas 906 in the building infrastructure 900. The central unit904 is communicatively coupled to a base station 908 to receive downlinkcommunications signals 910D from the base station 908. The central unit904 is communicatively coupled to a plurality of remote units 912 todistribute the downlink communications signals 910D to the remote units912 and to receive uplink communications signals 910U from the remoteunits 912, as previously discussed above. The downlink communicationssignals 910D and the uplink communications signals 910U communicatedbetween the central unit 904 and the remote units 912 are carried over ariser cable 914. The riser cable 914 may be routed through interconnectunits (ICUs) 916(1)-916(3) dedicated to each of the floors 902(1)-902(3)that route the downlink communications signals 910D and the uplinkcommunications signals 910U to the remote units 912 and also providepower to the remote units 912 via array cables 918.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps, or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat any particular order be inferred.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thespirit or scope of the invention. Since modifications, combinations,sub-combinations and variations of the disclosed embodimentsincorporating the spirit and substance of the invention may occur topersons skilled in the art, the invention should be construed to includeeverything within the scope of the appended claims and theirequivalents.

What is claimed is:
 1. A massive multiple-input multiple-output (M-MIMO)wireless distribution system (WDS), comprising: a plurality of remoteunits each configured to be deployed at a location to serve a respectiveremote coverage area; and a central unit communicatively coupled to eachof the plurality of remote units over a communications link among aplurality of communications links, the central unit being configured to:encode a received downlink communications signal to generate a downlinkMIMO communications signal; and distribute the downlink MIMOcommunications signal over the plurality of communications links to theplurality of remote units, wherein: each of the plurality of remoteunits comprises one or more antennas configured to distribute thedownlink MIMO communications signal to at least one client devicelocated in the respective remote coverage area; and at least one remoteunit among the plurality of remote units comprises a different number ofantennas from at least one other remote unit among the plurality ofremote units.
 2. The M-MIMO WDS of claim 1, wherein each of theplurality of remote units is further configured to form at least oneradio frequency (RF) beam to distribute the downlink MIMO communicationssignal to the at least one client device located in the respectiveremote coverage area.
 3. The M-MIMO WDS of claim 2, wherein a locationcorresponding to each of the plurality of remote units is determinedbased on client device density distribution throughout a coverage areaof the M-MIMO WDS to maximize system capacity of the M-MIMO WDS.
 4. TheM-MIMO WDS of claim 2, wherein the at least one remote unit among theplurality of remote units is configured to include a higher number ofthe antennas than the at least one other remote unit among the pluralityof remote units when the respective remote coverage area served by theat least one remote unit has a higher client device density than therespective remote coverage area served by the at least one other remoteunit among the plurality of remote units.
 5. The M-MIMO WDS of claim 2,wherein the at least one remote unit among the plurality of remote unitsis configured to include a lesser number of the antennas than the atleast one other remote unit among the plurality of remote units when therespective remote coverage area served by the at least one remote unithas a lower client device density than the respective remote coveragearea served by the at least one other remote unit among the plurality ofremote units.
 6. The M-MIMO WDS of claim 2, wherein the central unitcomprises electrical-to-optical (E/O) converters configured to convertthe downlink MIMO communications signal into a plurality of downlinkoptical fiber-based MIMO communications signals for distribution to theplurality of remote units over a downlink optical fiber-basedcommunications medium.
 7. The M-MIMO WDS of claim 2, wherein theplurality of remote units comprises remote unit optical-to-electrical(O/E) converters and remote unit E/O converters.
 8. The M-MIMO WDS ofclaim 1, wherein a location corresponding to each of the plurality ofremote units is determined based on client device density distributionthroughout a coverage area of the M-MIMO WDS to maximize system capacityof the M-MIMO WDS.
 9. The M-MIMO WDS of claim 1, wherein the at leastone remote unit among the plurality of remote units is configured toinclude a higher number of the antennas than the at least one otherremote unit among the plurality of remote units when the respectiveremote coverage area served by the at least one remote unit has a higherclient device density than the respective remote coverage area served bythe at least one other remote unit among the plurality of remote units.10. The M-MIMO WDS of claim 1, wherein the at least one remote unitamong the plurality of remote units is configured to include a lessernumber of the antennas than the at least one other remote unit among theplurality of remote units when the respective remote coverage areaserved by the at least one remote unit has a lower client device densitythan the respective remote coverage area served by the at least oneother remote unit among the plurality of remote units.
 11. The M-MIMOWDS of claim 1, wherein: each of the plurality of remote units isfurther configured to: receive at least one uplink communications signalfrom the at least one client device located in the respective remotecoverage area; and provide the at least one uplink communications signalto the central unit over the communications link among the plurality ofcommunications links; and the central unit is further configured tocombine a plurality of uplink communications signals received from theplurality of remote units.
 12. The M-MIMO WDS of claim 11, wherein thecentral unit comprises electrical-to-optical (E/O) converters configuredto convert the downlink MIMO communications signal into a plurality ofdownlink optical fiber-based MIMO communications signals fordistribution to the plurality of remote units over a downlink opticalfiber-based communications medium.
 13. The M-MIMO WDS of claim 12,wherein the plurality of remote units comprises: remote unitoptical-to-electrical (O/E) converters configured to convert theplurality of downlink optical fiber-based MIMO communications signalsinto the downlink MIMO communications signal; and remote unit E/Oconverters configured to convert the plurality of uplink communicationssignals into a plurality of uplink optical fiber-based communicationssignals.
 14. The M-MIMO WDS of claim 13, wherein the central unitfurther comprises O/E converters configured to convert the plurality ofuplink optical fiber-based communications signals received from theplurality of remote units into the plurality of uplink communicationssignals.
 15. A massive multiple-input multiple-output (M-MIMO) wirelessdistribution system (WDS), comprising: a plurality of remote units eachconfigured to be deployed at a location to serve a respective remotecoverage area, each remote unit including at least one remote unitoptical-to-electrical (O/E) converter and at least one remote unitelectrical-to-optical (E/O) converter; and a central unit opticallycoupled to the plurality of remote units over a plurality of opticalcommunications links, the central unit being configured to: encode areceived downlink communications signal to generate a downlink MIMOcommunications signal; and distribute the downlink MIMO communicationssignal over the plurality of communications links to the plurality ofremote units, wherein: each of the plurality of remote units comprisesone or more antennas configured to wirelessly distribute the downlinkMIMO communications signal to at least one client device located in therespective remote coverage area; and at least one remote unit among theplurality of remote units comprises a different number of antennas fromat least one other remote unit among the plurality of remote units. 16.The M-MIMO WDS of claim 15, wherein the remote unit O/E converters areconfigured to convert downlink MIMO communications signals intoelectrical downlink MIMO communications signals prior to wirelessdistribution to the respective remote coverage areas of the remoteunits.
 17. The M-MIMO WDS of claim 16, wherein a location correspondingto each of the plurality of remote units is determined based on clientdevice density distribution throughout a coverage area of the M-MIMO WDSto maximize system capacity of the M-MIMO WDS.
 18. The M-MIMO WDS ofclaim 16, wherein the at least one remote unit among the plurality ofremote units is configured to include a higher number of the antennasthan the at least one other remote unit among the plurality of remoteunits when the respective remote coverage area served by the at leastone remote unit has a higher client device density than the respectiveremote coverage area served by the at least one other remote unit amongthe plurality of remote units.
 19. The M-MIMO WDS of claim 16, whereinthe at least one remote unit among the plurality of remote units isconfigured to include a lesser number of the antennas than the at leastone other remote unit among the plurality of remote units when therespective remote coverage area served by the at least one remote unithas a lower client device density than the respective remote coveragearea served by the at least one other remote unit among the plurality ofremote units.
 20. A massive multiple-input multiple-output (M-MIMO)wireless distribution system (WDS), comprising: a plurality of remoteunits each configured to be deployed at a location to serve a respectiveremote coverage area; and a central unit communicatively coupled to theplurality of remote units over a plurality of optical communicationslinks, the central unit being configured to distribute a downlink MIMOcommunications signal over the plurality of optical communications linksto the plurality of remote units, wherein: each of the plurality ofremote units comprises one or more antennas configured to distribute thedownlink MIMO communications signal to at least one client devicelocated in the respective remote coverage area, and at least one remoteunit among the plurality of remote units comprises a higher number ofthe antennas than the at least one other remote unit among the pluralityof remote units when the respective remote coverage area served by theat least one remote unit has a higher client device density than therespective remote coverage area served by the at least one other remoteunit among the plurality of remote units.
 21. The M-MIMO WDS of claim20, wherein the plurality of remote units comprises remote unitoptical-to-electrical (O/E) converters configured convert opticalcommunications signals into electrical communications signals.
 22. TheM-MIMO WDS of claim 21, wherein at least one remote unit among theplurality of remote units is configured to include a lesser number ofthe antennas than the at least one other remote unit among the pluralityof remote units when the respective remote coverage area served by theat least one remote unit has a lower client device density than therespective remote coverage area served by the at least one other remoteunit among the plurality of remote units.